from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	1.783428	0.103273	NaN	0.000449	0.001783	brute	-1	1	0.663	0.166161	0.002700	0.687	10.733113	10.734530	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.598966	0.019955	NaN	0.000308	0.002599	brute	-1	5	0.757	0.166673	0.000619	0.742	15.593218	15.593326	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	1.949392	0.005666	NaN	0.000410	0.001949	brute	1	100	0.882	0.203702	0.001211	0.875	9.569806	9.569975	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.018911	0.000229	NaN	0.000042	0.018911	brute	1	100	1.000	0.007574	0.000126	0.000	2.496761	2.497104	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.601173	0.030045	NaN	0.000308	0.002601	brute	-1	100	0.882	0.203260	0.000372	0.875	12.797261	12.797283	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.022121	0.001863	NaN	0.000036	0.022121	brute	-1	100	1.000	0.007680	0.000881	0.000	2.880360	2.899244	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	1.951491	0.003758	NaN	0.000410	0.001951	brute	1	5	0.757	0.166382	0.000154	0.742	11.728976	11.728981	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.075387	0.001489	NaN	0.000744	0.001075	brute	1	1	0.663	0.165224	0.000455	0.687	6.508672	6.508697	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.590791	0.025237	NaN	0.000010	0.001591	brute	-1	1	0.896	0.025468	0.000160	0.967	62.461386	62.462622	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.381358	0.080133	NaN	0.000007	0.002381	brute	-1	5	0.922	0.026703	0.000093	0.974	89.177914	89.178461	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	1.871024	0.002125	NaN	0.000009	0.001871	brute	1	100	0.929	0.059930	0.001845	0.975	31.220029	31.234820	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.492094	0.032812	NaN	0.000006	0.002492	brute	-1	100	0.929	0.059388	0.000171	0.975	41.963136	41.963311	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	1.844899	0.001320	NaN	0.000009	0.001845	brute	1	5	0.922	0.026615	0.000164	0.974	69.317442	69.318753	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	0.975091	0.017602	NaN	0.000016	0.000975	brute	1	1	0.896	0.025576	0.000084	0.967	38.125586	38.125793	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.374	0.0	-1	1	0.044	0.003	0.245	0.245	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.396	0.0	-1	5	0.043	0.000	0.251	0.251	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.447	0.0	1	100	0.043	0.000	0.249	0.250	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.413	0.0	-1	100	0.043	0.000	0.251	0.251	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.268	0.0	1	5	0.043	0.000	0.253	0.253	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.441	0.0	1	1	0.043	0.001	0.249	0.249	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.380	0.0	-1	1	0.008	0.000	0.514	0.514	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.370	0.0	-1	5	0.008	0.000	0.527	0.527	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.379	0.0	1	100	0.008	0.000	0.508	0.508	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.377	0.0	-1	100	0.008	0.000	0.517	0.517	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.384	0.0	1	5	0.008	0.000	0.510	0.510	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.382	0.0	1	1	0.008	0.000	0.511	0.511	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.783	0.103	0.000	0.002	-1	1	0.166	0.003	10.733	10.735	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.002	0.000	0.021	-1	1	0.007	0.000	2.856	2.857	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.599	0.020	0.000	0.003	-1	5	0.167	0.001	15.593	15.593	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.004	0.000	0.024	-1	5	0.007	0.000	3.141	3.141	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.949	0.006	0.000	0.002	1	100	0.204	0.001	9.570	9.570	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	100	0.008	0.000	2.497	2.497	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.601	0.030	0.000	0.003	-1	100	0.203	0.000	12.797	12.797	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.002	0.000	0.022	-1	100	0.008	0.001	2.880	2.899	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.951	0.004	0.000	0.002	1	5	0.166	0.000	11.729	11.729	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	5	0.007	0.000	2.586	2.586	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.075	0.001	0.001	0.001	1	1	0.165	0.000	6.509	6.509	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.018	0.000	0.000	0.018	1	1	0.007	0.000	2.416	2.417	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.591	0.025	0.000	0.002	-1	1	0.025	0.000	62.461	62.463	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.001	0.000	0.004	-1	1	0.001	0.000	6.896	6.908	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.381	0.080	0.000	0.002	-1	5	0.027	0.000	89.178	89.178	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.005	0.000	0.007	-1	5	0.001	0.000	10.434	10.483	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.871	0.002	0.000	0.002	1	100	0.060	0.002	31.220	31.235	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.925	3.940	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.492	0.033	0.000	0.002	-1	100	0.059	0.000	41.963	41.963	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	0.000	0.006	-1	100	0.001	0.000	8.423	8.445	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.845	0.001	0.000	0.002	1	5	0.027	0.000	69.317	69.319	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.434	4.447	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.975	0.018	0.000	0.001	1	1	0.026	0.000	38.126	38.126	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.710	2.722	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.750415	1.013010	NaN	0.000107	0.000750	kd_tree	-1	1	0.929	0.094522	0.001854	0.910	7.939017	7.940544	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	0.980333	0.344856	NaN	0.000082	0.000980	kd_tree	-1	5	0.946	0.173177	0.002969	0.941	5.660885	5.661717	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	4.806605	0.401040	NaN	0.000017	0.004807	kd_tree	1	100	0.951	0.511724	0.005975	0.940	9.392964	9.393604	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	2.817059	0.201310	NaN	0.000028	0.002817	kd_tree	-1	100	0.951	0.505033	0.005633	0.940	5.577972	5.578318	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.502982	0.268727	NaN	0.000053	0.001503	kd_tree	1	5	0.946	0.174676	0.002490	0.941	8.604387	8.605261	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.831806	0.334570	NaN	0.000096	0.000832	kd_tree	1	1	0.929	0.094545	0.003628	0.910	8.797947	8.804424	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.026699	0.015512	NaN	0.000599	0.000027	kd_tree	-1	1	0.891	0.000389	0.000045	0.879	68.557773	69.010563	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.022506	0.000939	NaN	0.000711	0.000023	kd_tree	-1	5	0.911	0.000648	0.000010	0.905	34.734314	34.738774	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.035048	0.008406	NaN	0.000457	0.000035	kd_tree	1	100	0.894	0.004464	0.000048	0.917	7.850667	7.851114	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.036923	0.004802	NaN	0.000433	0.000037	kd_tree	-1	100	0.894	0.005557	0.001693	0.917	6.644180	6.945728	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.019788	0.000118	NaN	0.000809	0.000020	kd_tree	1	5	0.911	0.000661	0.000016	0.905	29.954087	29.963083	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.018422	0.000183	NaN	0.000869	0.000018	kd_tree	1	1	0.891	0.000372	0.000028	0.879	49.578292	49.722076	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.743	0.043	0.029	0.0	-1	1	0.703	0.103	3.900	3.942	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.287	0.057	0.024	0.0	-1	5	0.680	0.016	4.834	4.835	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.263	0.035	0.025	0.0	1	100	0.655	0.008	4.980	4.980	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.354	0.077	0.024	0.0	-1	100	0.673	0.016	4.986	4.988	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.337	0.035	0.024	0.0	1	5	0.665	0.010	5.019	5.020	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.247	0.046	0.025	0.0	1	1	0.680	0.017	4.775	4.776	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.002	0.016	0.0	-1	1	0.005	0.004	0.209	0.263	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	5	0.001	0.001	0.381	0.562	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	1	100	0.001	0.001	0.470	0.528	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	-1	100	0.001	0.000	0.656	0.656	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	5	0.001	0.000	0.645	0.646	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	1	1	0.001	0.000	0.664	0.664	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.750	1.013	0.000	0.001	-1	1	0.095	0.002	7.939	7.941	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	9.894	10.187	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.980	0.345	0.000	0.001	-1	5	0.173	0.003	5.661	5.662	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	8.272	8.592	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	4.807	0.401	0.000	0.005	1	100	0.512	0.006	9.393	9.394	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	4.576	4.724	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2.817	0.201	0.000	0.003	-1	100	0.505	0.006	5.578	5.578	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.651	6.827	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.503	0.269	0.000	0.002	1	5	0.175	0.002	8.604	8.605	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	5	0.000	0.000	3.902	4.081	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.832	0.335	0.000	0.001	1	1	0.095	0.004	8.798	8.804	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	4.026	4.233	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.016	0.001	0.000	-1	1	0.000	0.000	68.558	69.011	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	22.720	23.588	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.001	0.001	0.000	-1	5	0.001	0.000	34.734	34.739	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	25.066	26.077	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.008	0.000	0.000	1	100	0.004	0.000	7.851	7.851	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	6.204	6.490	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.037	0.005	0.000	0.000	-1	100	0.006	0.002	6.644	6.946	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	22.524	23.727	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	29.954	29.963	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.035	6.429	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.018	0.000	0.001	0.000	1	1	0.000	0.000	49.578	49.722	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.306	6.587	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.496	0.074	30	0.032	0.0	random	0.373	0.025	1.328	1.331	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.534	0.015	30	0.030	0.0	k-means++	0.402	0.025	1.327	1.330	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.145	0.218	30	0.155	0.0	random	2.470	0.052	2.083	2.084	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.273	0.018	30	0.152	0.0	k-means++	2.763	0.074	1.908	1.909	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.010	0.000	random	0.0	0.0	5.781	11.830	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	7.894	14.007	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	13.461	15.063	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	14.328	14.988	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.461	0.000	random	0.0	0.0	7.277	7.673	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	13.137	13.593	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.492	0.000	k-means++	0.0	0.0	6.865	7.261	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	12.759	13.100	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.001809	0.000249	20	0.008846	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000420	0.000022	-0.000965	4.303543	4.309574	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.001710	0.000074	20	0.009359	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000425	0.000031	-0.000750	4.018310	4.029129	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.002425	0.000102	20	0.329961	0.000002	full	random	20	300	1	1.000000e-16	0.278733	0.000918	0.000054	0.293767	2.640413	2.644915	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002428	0.000149	20	0.329529	0.000002	full	k-means++	20	300	1	1.000000e-16	0.317011	0.000925	0.000055	0.256968	2.624866	2.629577	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.072	0.000	20	0.002	0.0	random	0.026	0.002	2.769	2.778	See	See
3	KMeans_short	fit	10000	10000	2	0.210	0.003	20	0.001	0.0	k-means++	0.080	0.000	2.624	2.624	See	See
6	KMeans_short	fit	10000	10000	100	0.195	0.001	20	0.041	0.0	random	0.105	0.001	1.861	1.861	See	See
9	KMeans_short	fit	10000	10000	100	0.557	0.009	20	0.014	0.0	k-means++	0.307	0.002	1.813	1.813	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	4.304	4.310	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	13.664	14.014	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	4.018	4.029	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.504	12.883	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.330	0.000	random	0.001	0.0	2.640	2.645	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.418	10.613	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.330	0.000	k-means++	0.001	0.0	2.625	2.630	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.001	0.002	k-means++	0.000	0.0	11.523	11.712	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000357	0.000337	[20]	2.242799	3.566971e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000864	0.001722	0.55	0.412965	0.921140	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.001707	0.000445	[26]	4.686518	1.707024e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.004313	0.001317	0.28	0.395781	0.413832	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	10.159	0.554	[20]	0.079	0.000	1.695	0.033	5.994	5.996	See	See
3	LogisticRegression	fit	1000	1000	10000	0.916	0.772	[26]	0.087	0.001	0.724	0.029	1.266	1.267	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.243	0.0	0.001	0.002	0.413	0.921	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.017	0.0	0.000	0.000	0.351	0.357	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.687	0.0	0.004	0.001	0.396	0.414	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.189	0.0	0.001	0.000	0.111	0.111	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.009451	0.000278	NaN	8.464364	0.000009	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.016041	0.000363	0.122191	0.589217	0.589368	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.164	0.003	0.488	0.0	0.170	0.001	0.967	0.967	See	See
3	Ridge	fit	1000000	1000000	100	1.041	0.073	0.768	0.0	0.292	0.258	3.566	4.755	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.009	0.0	8.464	0.0	0.016	0.0	0.589	0.589	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.452	0.0	0.000	0.0	0.618	0.661	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	6.440	0.0	0.000	0.0	0.380	0.610	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.018	0.0	0.000	0.0	0.599	0.638	See	See